22 research outputs found
A Hybrid Approach for Aspect-Based Sentiment Analysis Using Deep Contextual Word Embeddings and Hierarchical Attention
The Web has become the main platform where people express their opinions
about entities of interest and their associated aspects. Aspect-Based Sentiment
Analysis (ABSA) aims to automatically compute the sentiment towards these
aspects from opinionated text. In this paper we extend the state-of-the-art
Hybrid Approach for Aspect-Based Sentiment Analysis (HAABSA) method in two
directions. First we replace the non-contextual word embeddings with deep
contextual word embeddings in order to better cope with the word semantics in a
given text. Second, we use hierarchical attention by adding an extra attention
layer to the HAABSA high-level representations in order to increase the method
flexibility in modeling the input data. Using two standard datasets (SemEval
2015 and SemEval 2016) we show that the proposed extensions improve the
accuracy of the built model for ABSA.Comment: Accepted for publication in the 20th International Conference on Web
Engineering (ICWE 2020), Helsinki Finland, 9-12 June 202
Does BERT understand sentiment? Leveraging comparisons between contextual and non-contextual embeddings to improve aspect-based sentiment models
When performing Polarity Detection for different words in a sentence, we need to look at the words around to understand the sentiment. Massively pretrained language models like BERT can encode not only just the words in a document but also the context around the words along with them. This begs the questions, "Does a pretrain language model also automatically encode sentiment information about each word?" and "Can it be used to infer polarity towards different aspects?". In this work we try to answer this question by showing that training a comparison of a contextual embedding from BERT and a generic word embedding can be used to infer sentiment. We also show that if we finetune a subset of weights the model built on comparison of BERT and generic word embedding, it can get state of the art results for Polarity Detection in Aspect Based Sentiment Classification datasets
Does BERT Understand Sentiment? Leveraging Comparisons Between Contextual and Non-Contextual Embeddings to Improve Aspect-Based Sentiment Models
When performing Polarity Detection for different words in a sentence, we need
to look at the words around to understand the sentiment. Massively pretrained
language models like BERT can encode not only just the words in a document but
also the context around the words along with them. This begs the questions,
"Does a pretrain language model also automatically encode sentiment information
about each word?" and "Can it be used to infer polarity towards different
aspects?". In this work we try to answer this question by showing that training
a comparison of a contextual embedding from BERT and a generic word embedding
can be used to infer sentiment. We also show that if we finetune a subset of
weights the model built on comparison of BERT and generic word embedding, it
can get state of the art results for Polarity Detection in Aspect Based
Sentiment Classification datasets
CL-XABSA: Contrastive Learning for Cross-lingual Aspect-based Sentiment Analysis
As an extensive research in the field of Natural language processing (NLP),
aspect-based sentiment analysis (ABSA) is the task of predicting the sentiment
expressed in a text relative to the corresponding aspect. Unfortunately, most
languages lack of sufficient annotation resources, thus more and more recent
researchers focus on cross-lingual aspect-based sentiment analysis (XABSA).
However, most recent researches only concentrate on cross-lingual data
alignment instead of model alignment. To this end, we propose a novel
framework, CL-XABSA: Contrastive Learning for Cross-lingual Aspect-Based
Sentiment Analysis. Specifically, we design two contrastive strategies, token
level contrastive learning of token embeddings (TL-CTE) and sentiment level
contrastive learning of token embeddings (SL-CTE), to regularize the semantic
space of source and target language to be more uniform. Since our framework can
receive datasets in multiple languages during training, our framework can be
adapted not only for XABSA task, but also for multilingual aspect-based
sentiment analysis (MABSA). To further improve the performance of our model, we
perform knowledge distillation technology leveraging data from unlabeled target
language. In the distillation XABSA task, we further explore the comparative
effectiveness of different data (source dataset, translated dataset, and
code-switched dataset). The results demonstrate that the proposed method has a
certain improvement in the three tasks of XABSA, distillation XABSA and MABSA.
For reproducibility, our code for this paper is available at
https://github.com/GKLMIP/CL-XABSA
Recolha, extração e classificação de opiniões sobre aplicações lúdicas para saúde e bem-estar
Nowadays, mobile apps are part of the life of anyone who owns a smartphone.
With technological evolution, new apps come with new features, which brings a
greater demand from users when using an application. Moreover, at a time when
health and well-being are a priority, more and more apps provide a better user
experience, not only in terms of health monitoring but also a pleasant experience
in terms of entertainment and well-being. However, there are still some limitations
regarding user experience and usability. What can best translate user satisfaction
and experience are application reviews. Therefore, to have a perception of the most
relevant aspects of the current applications, a collection of reviews and respective
classifications was performed.
This thesis aims to develop a system that allows the presentation of the most relevant
aspects of a given health and wellness application after collecting the reviews
and later extracting the aspects and classifying them. In the reviews collection task,
two Python libraries, one for the Google Play Store and one for the App Store, provide
methods for extracting data about an application. For the extraction and
classification of aspects, the LCF-ATEPC model was chosen given its performance
in aspects-based sentiment analysis studies.Atualmente, as aplicações móveis fazem parte da vida de qualquer pessoa que possua
um smartphone. Com a evolução tecnológica, novas aplicações surgem com
novas funcionalidades, o que traz uma maior exigência por parte dos utilizadores
quando usam uma aplicação. Numa altura em que a saúde e bem-estar são uma
prioridade, existem cada vez mais aplicações com o intuito de providenciar uma
melhor experiência ao utilizador, não só a nível de monitorização de saúde, mas
também de uma experiência agradável em termos de entertenimento e bem estar.
Contudo, existem ainda algumas limitações no que toca à experiência e usabilidade
do utilizador. O que melhor pode traduzir a satisfação e experiência do utilizador
são as reviews das aplicações. Assim sendo, para ter uma perceção dos aspetos
mais relevantes das atuais aplicações, foi feita uma recolha das reviews e respetivas
classificações.
O objetivo desta tese consiste no desenvolvimento de um sistema que permita
apresentar os aspetos mais relevantes de uma determinada aplicação de saúde e
bem estar, após a recolha das reviews e posterior extração dos aspetos e classificação
dos mesmos. No processo de recolha de reviews, foram usadas duas
bibliotecas em Python, uma relativa à Google Play Store e outra à App Store, que
providenciam métodos para extrair dados relativamente a uma aplicação. Para a
extração e classificação dos aspetos, o modelo LCF-ATEPC foi o escolhido dada a
sua performance em estudos de análise de sentimento baseada em aspectos.Mestrado em Engenharia de Computadores e Telemátic
A Survey on Semantic Processing Techniques
Semantic processing is a fundamental research domain in computational
linguistics. In the era of powerful pre-trained language models and large
language models, the advancement of research in this domain appears to be
decelerating. However, the study of semantics is multi-dimensional in
linguistics. The research depth and breadth of computational semantic
processing can be largely improved with new technologies. In this survey, we
analyzed five semantic processing tasks, e.g., word sense disambiguation,
anaphora resolution, named entity recognition, concept extraction, and
subjectivity detection. We study relevant theoretical research in these fields,
advanced methods, and downstream applications. We connect the surveyed tasks
with downstream applications because this may inspire future scholars to fuse
these low-level semantic processing tasks with high-level natural language
processing tasks. The review of theoretical research may also inspire new tasks
and technologies in the semantic processing domain. Finally, we compare the
different semantic processing techniques and summarize their technical trends,
application trends, and future directions.Comment: Published at Information Fusion, Volume 101, 2024, 101988, ISSN
1566-2535. The equal contribution mark is missed in the published version due
to the publication policies. Please contact Prof. Erik Cambria for detail
Interpretable Architectures and Algorithms for Natural Language Processing
Paper V is excluded from the dissertation with respect to copyright.This thesis has two parts: Firstly, we introduce the human level-interpretable models using Tsetlin Machine (TM) for NLP tasks. Secondly, we present an interpretable model using DNNs. The first part combines several architectures of various NLP tasks using TM along with its robustness. We use this model to propose logic-based text classification. We start with basic Word Sense Disambiguation (WSD), where we employ TM to design novel interpretation techniques using the frequency of words in the clause. We then tackle a new problem in NLP, i.e., aspect-based text classification using a novel feature engineering for TM. Since TM operates on Boolean features, it relies on Bag-of-Words (BOW), making it difficult to use pre-trained word embedding like Glove, word2vec, and fasttext. Hence, we designed a Glove embedded TM to significantly enhance the model’s performance. In addition to this, NLP models are sensitive to distribution bias because of spurious correlations. Hence we employ TM to design a robust text classification against spurious correlations.
The second part of the thesis consists interpretable model using DNN where we design a simple solution for complex position dependent NLP task. Since TM’s interpretability comes with the cost of performance, we propose an DNN-based architecture using a masking scheme on LSTM/GRU based models that ease the interpretation for humans using the attention mechanism. At last, we take the advantages of both models and design an ensemble model by integrating TM’s interpretable information into DNN for better visualization of attention weights.
Our proposed model can be efficiently integrated to have a fully explainable model for NLP that assists trustable AI. Overall, our model shows excellent results and interpretation in several open-sourced NLP datasets. Thus, we believe that by combining the novel interpretation of TM, the masking technique in the neural network, and the integrated ensemble model, we can build a simple yet effective platform for explainable NLP applications wherever necessary.publishedVersio
Natural Language Processing: Emerging Neural Approaches and Applications
This Special Issue highlights the most recent research being carried out in the NLP field to discuss relative open issues, with a particular focus on both emerging approaches for language learning, understanding, production, and grounding interactively or autonomously from data in cognitive and neural systems, as well as on their potential or real applications in different domains